The R Toolkit

ECI 588: Text Mining in Education
Unit 1: Week 2 Code-Along

Adapted from:
Data Science in Education Using R - Chapter 6
https://datascienceineducation.com
Data Science in a Box - Hello World!
https://datasciencebox.com

Today's Agenda

R Tools

  1. Numbers & Operators
  2. Objects & Data
  3. Functions & Arguments
  4. Packages
  5. RStudio Projects

Bring it Together

  1. Project Setup
  2. Exploring Data
  3. Pipe Operator
  4. Assignment vs. Equality
  5. Object Naming

1. NUMBERS & OPERATORS

Arithmetic, Logic, & Assignment

R is a Calculator

Numbers and basic arithmetic operators (+ , - , *, /) behave as expected.

Type this in your console: 

2 + 3

You'll get this:

[1] 5

Arithmetic Operators project icon

R is Logical

R can also perform logical operations.

What happens when you type this in your console? 

5 > 3

Or this?

5 == 3

What symbol seems to be missing? Or rather has been replaced?

Logical Operators project icon

The Assignment Operator(s)

Bad Form

my_variable = 2 + 3

my_variable

[1] 5

Good Form

my_dog <- "Howie"

my_dog

[1] "Howie"

Other Essential Operator(s)

The Dollar $ign Used to select variables. 

trees$Height

[1] 70 65 63 72 81 83

Now you try:
Select Girth,
assign to variable tree_girth,
print to console.

The Question Mark?
Used to get help. 

?trees

trees icon

2. OBJECTS & DATA

Everything is an object in R!

We'll be creating a lot of them…

Especially objects of the class data frame

Data Types

Numeric

my_sum <- 3+3

class(my_sum)

[1] "numeric"

print(my_sum)

[1] 6

Character

my_cat <- "Muffins"

class(my_cat)

[1] "character"

my_cat

[1] "Muffins"

Data Types Cont.

Vector

my_vector <- c(1,2,3,4)

class(my_vector)

[1] "numeric"

print(my_vector)

[1] 1 2 3 4

List

my_list <- c(1,"Kat", 3)

class(my_list)

[1] "character"

my_list

[1] "1"   "Kat" "3"

Data Types Cont.

Matrix

my_matrix <- matrix(1:9, nrow = 3, ncol = 3)

print(my_matrix)

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

Data Frame

my_df <- trees

head(my_df)

  Girth Height Volume
1   8.3     70   10.3
2   8.6     65   10.3
3   8.8     63   10.2
4  10.5     72   16.4
5  10.7     81   18.8
6  10.8     83   19.7

project icon

Objects Concluded

Aside from being a certain class, objects can have have other attributes, such column names and dimensions:

colnames(my_df)

[1] "Girth"  "Height" "Volume"

dim(my_df)

[1] 31  3

And can even be used with operators! Try this:

my_sum * my_matrix

3. FUNCTIONS

Functions are pre-packaged pieces of code that (typically) start with a verb, followed by objects or inputs in parentheses called “arguments”:

do_this(to_this)  
do_that(to_this, to_that, with_those)

Let's make a function!

Basic template for creating a function: 

name_of_function <- function(argument_1, argument_2){
    code_that_does_something
    code_that_does_something_else
}

Try making this basic addition function: 

add_numbers <- function(number_1, number_2) {
    number_1 + number_2
}

Let's test it!

Use your new function to add 1 and 2:

add_numbers(1, 2)

[1] 3

What happens if we try add the objects we created earlier?

add_numbers(my_sum, my_matrix)

function icon

4. PACKAGES

Collections of R code that contain functions, data, and/or documentation.

The tidyverse is an opinionated collection of R packages designed for data science we'll use a lot.

project icon

Let's get some packages!

Installing a Package

# template for installing a package
install.packages("package_name")

# example of installing a package
install.packages("tidytext")

Loading a Package

# template for loading a package
library(package_name)

# example of loading a package
libary(tidytext)

Or just point and click...

project icon

Let's get these installed too...

5. RSTUDIO PROJECTS

Your home for code, files, reports and more.

Why RStudio Projects?

  • Projects create a shareable self-contained folder for your:
    • files, images, reports, code, and subfolders

  • Eliminates the need for computer specific files paths like this:

    /Volumes/GoogleDrive/My Drive/College of Ed/Learning Analytics/Courses/ECI 588 Text Mining/R/eci-588/unit-1/img/project.png

  • And replaces with this, which anyone can run:

    unit-1/img/project.png

6. DSIEUR Walkthrough

Caption

Link: https://datascienceineducation.com

Creating a Project and R Script

Live Demo

Installing Packages

Copy and paste from DSIEUR:

# install devtools
install.packages("devtools", repos = "http://cran.us.r-project.org")

# install the dataedu package (requires R version 3.6 or higher)
devtools::install_github("data-edu/dataedu")

# Installing the skimr package, not included in {dataedu}
install.packages("skimr")

Loading Packages

Let's set up our environment:

library(tidyverse)

library(dataedu)

library(skimr)

library(janitor)

What do you think running the above lines of code accomplished?

How do you know?

What do those conflicts mean?

Using the "masked" functions...

Just specify the package followed by ::

# using the filter() function from the stats package
x <- 1:100

stats::filter(x, rep(1, 3))

# using the filter() function from the dplyr package
starwars %>% 
    dplyr::filter(mass > 85)

Loading dataedu Data

dataedu::ma_data_init

dataedu::ma_data_init -> ma_data

ma_data_init <- dataedu::ma_data_init

Exploring Data and Common Errors

names(ma_data_init)

glimpse(ma_dat_init) 

glimpse(ma_data_init)

summary(ma_data_init)

glimpse(ma_data_init$Town)

summary(ma_data_init$Town)

glimpse(ma_data_init$AP_Test Takers)

glimpse(ma_data_init$`AP_Test Takers`)

summary(ma_data_init$`AP_Test Takers`)

The Pipe Operator

ma_data_init %>% 
    group_by(District Name) %>%  
    count()

ma_data_init %>% 
    group_by(`District Name`) %>% 
    count()  

ma_data_init %>% 
    group_by(`District Name`) %>% 
    count() %>% 
    filter(n > 10)

"Reading" Code

ma_data_init %>% 
    group_by(`District Name`) %>% 
    count() %>% 
    filter(n > 10) %>% 
    arrange(desc(n))

Take the ma_data_init dataset and then
group it by District Name and then
count (the number of schools in a district) and then
filter for Districts with more than 10 schools and then
arrange the list of Districts and the number of schools in each District in descending order, based on the number of schools.

Exploring Assignment vs. Equality

ma_data_init %>%
    group_by(`District Name`) %>%
    count() %>%
    filter(n = 10) 

ma_data_init %>%
    group_by(`District Name`) %>%
    count() %>%
    filter(n == 10) 

ma_data_init %>% 
    rename(district_name = `District Name`,
           grade = Grade) %>% 
    select(district_name, grade)

Why the error for the second and not the others?

Object and Variable Names Basics

ma data <- 
    ma_data_init %>% 
    clean_names()

01_ma_data <- 
    ma_data_init %>%  
    clean_names()

$_ma_data <- 
    ma_data_init %>%  
    clean_names()

ma_data_01 <- 
    ma_data_init %>% 
    clean_names()

MA_data_02 <- 
    ma_data_init %>% 
    clean_names()

LEARN MORE

Required:
Chapters 5 & 6 of DSIEUR

Recommended:
Swirl
LinkedIn Learning